Papers by Seongmin Park
Enhancing Time Awareness in Generative Recommendation (2025.findings-emnlp)
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| Challenge: | Existing models focus on sequential order of items and neglect to handle temporal dynamics . existing models neglect to capture hidden user preferences via various temporal signals . |
| Approach: | They propose a model that generates recommendations into a text-to-text generation task . they introduce Time-aware Prompting and Trend-awful Inference . |
| Outcome: | The proposed model outperforms state-of-the-art models with gains of 15.4% and 14.3% . it is based on time-aware Prompting and Trend-awful Inference . |
RT-VQ2A2: Real Time Vector Quantized Question Answering with ASR (2024.lrec-main)
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| Challenge: | Existing frameworks for QA with large language models are difficult to implement due to noise, limited context length and latency. |
| Approach: | They propose a model-agnostic framework to address problems in QA with large language models. |
| Outcome: | The proposed framework reduces noise in the ASR output and the limited context length of LLMs and improves performance on the widely used Spoken-SQuAD dataset. |
LIME: Weakly-Supervised Text Classification without Seeds (2022.coling-1)
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| Challenge: | Existing approaches to weakly-supervised text classification use only label names as sources of supervision. |
| Approach: | They propose a framework for weakly-supervised text classification that replaces seed-word generation with entailment-based pseudo-classification. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art in 4 benchmarks. |
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment (2024.acl-long)
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| Challenge: | Quantization-aware direct preference optimization (QDPO) improves conversational abilities of quantized LLMs . token-flipping is a critical factor for degraded text generation quality . |
| Approach: | They propose a method that aligns quantized LLMs with their full-precision counterparts, improving conversational abilities. |
| Outcome: | The proposed method outperforms established fine-tuning techniques on two instruction-tuned LLMs in various languages and models, setting a new benchmark for conversational chatbot development. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification with extremely weak supervision impose stricter supervision constraints than those under regular weak supervision. |
| Approach: | They propose a framework that creates weak labels by leveraging recent developments in zero-shot text classification. |
| Outcome: | The proposed framework outperforms existing methods on weak labels generated by weakly supervise classification. |
Non-Essential Is NEcessary: Order-agnostic Multi-hop Question Generation (2024.lrec-main)
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| Challenge: | Existing multi-hop question generation methods treat answer-irrelevant documents as non-essential and remove them as impurities, which can lead to a decrease in model performance. |
| Approach: | They propose a task which leverages non-essential data in the training phase to create a robust model and extract the consistent embeddings in real-world inference environments. |
| Outcome: | The proposed model can perform ranker and generator without external modules and achieves state-of-the-art on a hotpotQA dataset. |
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers (2023.eacl-main)
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| Challenge: | Quantization-aware training (QAT) is a promising method to lower the implementation cost and energy consumption. |
| Approach: | They propose a method for fast converging QAT of pre-trained Transformers using a layer-wise signal propagation method with the intact signal from the teacher. |
| Outcome: | The proposed method achieves superior accuracy with significantly lower fine-tuning iterations on well-known Transformers of natural language processing as well as computer vision compared to the state-of-the-art methods. |
Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025.naacl-long)
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| Challenge: | Existing CRSs assume positive and negative user preferences, but assume that the entities in the dialogue history are positive. |
| Approach: | They propose a conversational recommender model that captures user sentiments and uses the reasoning capacity of the LLMs to extract user's hidden preferences. |
| Outcome: | The proposed model outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. |